Advanced Multi-Objective Wireless Sensor Network (WSN) Optimization Framework

A high-tier, mathematically rigorous Computational Intelligence & Network Engineering Toolkit engineered in Python. This repository delivers a unified empirical benchmarking pipeline designed to solve the Constrained Multi-Objective Wireless Sensor Network (WSN) Deployment Challenge by contrasting four state-of-the-art evolutionary frameworks.

By orchestrating the Platypus evolutionary library, the framework evaluates Pareto-optimality layouts across unified hypervolume indicator metrics, removing high-frequency node distribution errors.

---

🛠️ Technology Stack & Requirements

• Evolutionary Engine: Platypus-Opt Framework
• Matrix Calculus: NumPy
• Visualization Suite: Matplotlib (Advanced scientific styling)

---

📐 Optimization Paradigm & Mathematical Objectives

The sensor deployment loop balances two highly conflicting spatial objectives over a multi-dimensional continuous geographical coordinate field:

1. Maximize Area Coverage ($f_1$)

Enforces that the overall sensing intersection fields of the $N$ deployed wireless nodes encapsulate the maximum possible grid points within the operational environment boundaries:

$$\max f_1(X) = \frac{\text{Total Grid Coordinate Points Covered}}{\text{Total Grid Coordinate Points In Environment}} \times 100$$

2. Maximize Network Connectivity ($f_2$)

Ensures that the active nodes maintain localized proximity metrics beneath their maximum radio transmission ranges, protecting operational communication channels back to the sink station:

$$\max f_2(X) = \frac{\text{Active Connected Communication Node Pairs}}{\text{Total Possible Structural Node Pairs}} \times 100$$

---

🚀 Unified Algorithmic Execution Core

• wsn_multi_objective_solver.py: A comprehensive, multi-threaded benchmarking script that runs 50 sensor node parameters over 10,000 function evaluation limits. It executes parallel optimization tracking blocks across:
  1. NSGA-II: Non-dominated Sorting Genetic Algorithm II (Crowding distance sorting).
  2. NSGA-III: Reference-point-based Non-dominated Sorting Genetic Algorithm.
  3. MOEA/D: Multi-Objective Evolutionary Algorithm based on Decomposition.
  4. SPEA2: Strength Pareto Evolutionary Algorithm 2 (Density-based preservation).

---

📊 Empirical Pareto Front Evaluation & Hypervolume Metrics

The framework automatically scores the non-dominated vector distributions by checking the exact mathematical Hypervolume (HV) area relative to a worst-case reference coordinate anchor.

The comparative output showcases the convergence velocity and uniform diversification of each evolutionary block across the coverage vs. connectivity trade-off spectrum.

---

💻 Local Workspace Verification

1. Replicate Project Infrastructure

git clone [https://github.com/mrhashx/wsn-multi-objective-optimization-evolutionary.git](https://github.com/mrhashx/wsn-multi-objective-optimization-evolutionary.git)
cd wsn-multi-objective-optimization-evolutionary

2. Execute Benchmark Solver

Executing the unified pipeline triggers the multi-algorithm loop, calculates hypervolume trends, and outputs the comparative subplots graph directly:

python wsn_multi_objective_solver.py